JP6431044B2 - Biometric authentication device, biometric authentication method, and program - Google Patents

Biometric authentication device, biometric authentication method, and program Download PDF

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JP6431044B2
JP6431044B2 JP2016509684A JP2016509684A JP6431044B2 JP 6431044 B2 JP6431044 B2 JP 6431044B2 JP 2016509684 A JP2016509684 A JP 2016509684A JP 2016509684 A JP2016509684 A JP 2016509684A JP 6431044 B2 JP6431044 B2 JP 6431044B2
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鈴木　智晴
智晴 鈴木
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Description

本開示の実施形態は、生体認証の技術に関する。  Embodiments of the present disclosure relate to biometric authentication technology.

既存の生体認証装置では、撮影後の画像から抽出される生体情報と、予め登録されている生体情報とが互いに一致する場合、本人であると判定する。この生体情報には掌紋や静脈などを示す特徴が含まれており、静脈を示す特徴を用いて生体認証を行う場合は、撮影後の画像から掌紋を示す特徴を分離して、できるだけ静脈を示す特徴のみにする必要がある。掌紋を示す特徴を分離する方法として、例えば、偏光フィルタなどを利用して光学的に分離する方法が知られている。また、他の方法として、例えば、複数波長撮影による方法が知られている。  In the existing biometric authentication device, when the biometric information extracted from the image after photographing and the biometric information registered in advance match each other, it is determined that the user is the person himself / herself. This biometric information includes features such as palm prints and veins. When biometric authentication is performed using the features indicating veins, the features indicating the palm prints are separated from the captured image to indicate the veins as much as possible. It needs to be a feature only. As a method of separating the feature indicating the palm print, for example, a method of optical separation using a polarizing filter or the like is known. As another method, for example, a method using multiple wavelength imaging is known.

A.Ross, A.K.Jain, and J.Reisman, “A Hybrid fingerprint matcher”, Pattern Recognition, vol.36, no.7, pp.1661-1673, 2003.A. Ross, A. K. Jain, and J. Reisman, “A Hybrid fingerprint matcher”, Pattern Recognition, vol. 36, no. 7, pp. 1661-1673, 2003.

しかしながら、掌紋を示す特徴を物理的に分離する方法を適用できない場合では、掌紋を示す特徴が含まれたままの生体情報を用いて生体認証を行わなくてはならない。掌紋を示す特徴の多様性は、静脈を示す特徴の多様性に比べて乏しいため、生体情報に含まれる掌紋を示す特徴が多い程、他人受入率（FAR（False Acceptance Rate））を高めてしまう。また、メラニンが強く沈着した掌紋を示す特徴が含まれている場合、静脈を示す特徴に比べて掌紋を示す特徴が抽出され易くなるため、他人受入率をさらに高めてしまう。  However, when the method of physically separating the feature indicating the palm print cannot be applied, the biometric authentication must be performed using the biometric information including the feature indicating the palm print. Because the diversity of features that indicate palm prints is less than the variety of features that indicate veins, the more features that indicate palm prints included in biological information, the higher the acceptance rate (FAR (False Acceptance Rate)). . In addition, when a feature indicating a palm print in which melanin is strongly deposited is included, a feature indicating a palm print is more easily extracted than a feature indicating a vein, and the acceptance rate of others is further increased.

本開示の実施形態では、画像から掌紋を示す特徴を物理的に分離する方法を適用できない場合であっても、他人受入率が高くなることを抑えることが可能な生体認証装置、生体認証方法、及びプログラムを提供することを目的とする。  In the embodiment of the present disclosure, a biometric authentication device, a biometric authentication method, and a biometric authentication method capable of suppressing an increase in the acceptance rate of others even when a method of physically separating a feature indicating a palmprint from an image cannot be applied. And to provide a program.

本開示の実施形態の生体認証装置は、入力される画像から複数の指向的特徴を抽出するフィルタと、前記フィルタにより抽出される複数の指向的特徴に対して静脈に相当する輝度値よりも大きくなる傾向にある掌紋に相当する輝度値が静脈に相当する輝度値に比べて小さくなるように正規化を行う方向毎指向的特徴正規化処理部と、前記方向毎指向的特徴正規化処理部から出力される複数の指向的特徴により無指向的特徴を生成する無指向的特徴生成部と、前記無指向的特徴と記憶部に記憶されている登録無指向的特徴との類似度を求める照合処理部と、前記類似度を用いて本人であるか否かの判定を行う判定部とを備える。 Biometric authentication apparatus of an embodiment of the present disclosure includes a filter for extracting a plurality of directional characteristics from the image input is greater than the brightness value corresponding to the vein to a plurality of directional features extracted by said filter A direction-specific feature normalization processing unit that normalizes so that a luminance value corresponding to a palm print that tends to become smaller than a luminance value corresponding to a vein, and the direction-specific feature normalization processing unit A non-directional feature generation unit that generates an omni-directional feature from a plurality of output directional features, and a collation process for obtaining a similarity between the omni-directional feature and a registered omni-directional feature stored in the storage unit And a determination unit that determines whether or not the user is the person using the similarity.

また、本開示の実施形態の生体認証方法は、コンピュータが、入力される画像から複数の指向的特徴を抽出し、前記抽出した複数の指向的特徴に対して静脈に相当する輝度値よりも大きくなる傾向にある掌紋に相当する輝度値が静脈に相当する輝度値に比べて小さくなるように正規化を行い、前記正規化した複数の指向的特徴により無指向的特徴を生成し、前記無指向的特徴と記憶部に記憶されている登録無指向的特徴との類似度を求め、前記類似度を用いて本人であるか否かの判定を行う。 In the biometric authentication method according to the embodiment of the present disclosure, the computer extracts a plurality of directional features from an input image, and the extracted plurality of directional features is larger than a luminance value corresponding to a vein. Normalization is performed such that a luminance value corresponding to a palmprint that tends to become smaller than a luminance value corresponding to a vein, and an omnidirectional feature is generated by the plurality of normalized directional features. The similarity between the target feature and the registered omnidirectional feature stored in the storage unit is obtained, and it is determined whether or not the user is the person using the similarity.

また、本開示の実施形態のプログラムは、コンピュータに、入力される画像から複数の指向的特徴を抽出し、前記抽出した複数の指向的特徴に対して静脈に相当する輝度値よりも大きくなる傾向にある掌紋に相当する輝度値が静脈に相当する輝度値に比べて小さくなるように正規化を行い、前記正規化した複数の指向的特徴により無指向的特徴を生成し、前記無指向的特徴と記憶部に記憶されている登録無指向的特徴との類似度を求め、前記類似度を用いて本人であるか否かの判定を行うことを実行させる。 Further, the program according to the embodiment of the present disclosure extracts a plurality of directional features from an image input to a computer, and the extracted plurality of directional features tend to be larger than a luminance value corresponding to a vein. Normalization is performed such that a luminance value corresponding to a palm print in the image becomes smaller than a luminance value corresponding to a vein, and an omnidirectional feature is generated by the normalized directional features, and the omnidirectional feature is generated. And the registration omni-directional feature stored in the storage unit are obtained, and it is executed to determine whether or not the person is the person using the similarity.

本開示の実施形態によれば、画像から掌紋を示す特徴を物理的に分離する方法を適用できない場合であっても、他人受入率が高くなることを抑えることができる。  According to the embodiment of the present disclosure, it is possible to suppress an increase in the acceptance rate of others even when a method of physically separating a feature indicating a palm print from an image cannot be applied.

本開示の実施形態の生体認証装置の一例を示す図である。It is a figure which shows an example of the biometrics apparatus of embodiment of this indication. 本開示の実施形態の生体認証方法を示すフローチャートである。It is a flowchart which shows the biometrics authentication method of embodiment of this indication. 本開示の実施形態の特徴抽出部の一例を示す図である。It is a figure which shows an example of the feature extraction part of embodiment of this indication. 本開示の実施形態の照合処理部の一例を示す図である。It is a figure which shows an example of the collation process part of embodiment of this indication. 生体認証装置のハードウェアの一例を示す図である。It is a figure which shows an example of the hardware of a biometrics authentication apparatus.

図１は、本開示の実施形態の生体認証装置の一例を示す図である。
図１に示す生体認証装置１は、画像取得部２と、領域特定部３と、特徴抽出部４と、照合処理部５と、スコア判定部６（判定部）と、記憶部７とを備える。FIG. 1 is a diagram illustrating an example of a biometric authentication device according to an embodiment of the present disclosure.
A biometric authentication device 1 illustrated in FIG. 1 includes an image acquisition unit 2, a region specification unit 3, a feature extraction unit 4, a matching processing unit 5, a score determination unit 6 (determination unit), and a storage unit 7. .

図２は、本開示の実施形態の生体認証方法を示すフローチャートである。
まず、画像取得部２は、被験者の手の画像を取得する（Ｓ１）。例えば、画像取得部２は、撮像装置であって、単板の撮像素子とベイヤー配列のＲＧＢの各カラーフィルタにより被検者の手の撮像画像を取得する。FIG. 2 is a flowchart illustrating a biometric authentication method according to an embodiment of the present disclosure.
First, the image acquisition unit 2 acquires an image of a subject's hand (S1). For example, the image acquisition unit 2 is an imaging device, and acquires a captured image of a hand of a subject using a single-plate imaging element and RGB color filters in a Bayer array.

次に、領域特定部３は、画像取得部２で取得される画像において被検者の手のひらに相当する手のひら領域（ROI(Region Of Interest)）を特定する（Ｓ２）。  Next, the region specifying unit 3 specifies a palm region (ROI (Region Of Interest)) corresponding to the palm of the subject in the image acquired by the image acquiring unit 2 (S2).

次に、特徴抽出部４は、領域特定部３により特定される手のひら領域の画像ｆから無指向的特徴を抽出する（Ｓ３）。なお、無指向的とは、ある画像ｆに対してフィルタ処理Ｓを行う場合において、フィルタ処理Ｓの前に様々な角度θに対する画像回転変換処理ＴθやＴθの逆変換処理Ｔθ −１を挿入した場合であっても、フィルタ処理Ｓ単体の結果とほとんど変わらないことと定義する。すなわち、記号であらわすと、無指向的とは、任意の角度θにおいて、Ｓ（ｆ）＝Ｔθ −１（Ｓ（Ｔθ（ｆ）））であると定義する。Next, the feature extraction unit 4 extracts an omnidirectional feature from the palm region image f specified by the region specification unit 3 (S3). Note that the non-directional, in the case of performing a filtering process S to a certain image f, the inverse conversion processing of the image rotation transformation processing T theta and T theta for various angles theta before filtering S T theta -1 It is defined that even when the is inserted, the result is almost the same as the result of the filtering process S alone. That is, when expressed by a symbol, non-directional is defined as S (f) = T θ −1 (S (T θ (f))) at an arbitrary angle θ.

次に、照合処理部５は、特徴抽出部４により抽出される無指向的特徴と、予め登録され記憶部７に記憶されている登録無指向的特徴との類似度を求める（Ｓ４）。  Next, the matching processing unit 5 obtains a similarity between the omnidirectional feature extracted by the feature extraction unit 4 and the registered omnidirectional feature registered in advance and stored in the storage unit 7 (S4).

次に、スコア判定部６は、照合処理部５により求められる類似度により本人であるか否かの判定を行う（Ｓ５）。  Next, the score determination unit 6 determines whether or not the person is the person based on the similarity obtained by the matching processing unit 5 (S5).

図３は、本開示の実施形態の特徴抽出部４の一例を示す図である。
図３に示す特徴抽出部４は、フィルタ４１と、方向毎指向的特徴正規化処理部４２と、各点最大値選択（point-wise maximum）部４３と、２値化部４４と、細線化部４５とを備える。FIG. 3 is a diagram illustrating an example of the feature extraction unit 4 according to the embodiment of the present disclosure.
The feature extraction unit 4 shown in FIG. 3 includes a filter 41, a direction-specific feature normalization processing unit 42, a point-wise maximum selection unit 43, a binarization unit 44, and a thinning process. Part 45.

フィルタ４１は、入力される手のひら領域の画像ｆに対して、８つの方向θ（0°、22.5°、45°、67.5°、90°、112.5°、135°、157.5°）毎にGaborフィルタ処理を行い、それぞれのフィルタ応答（輝度値）を指向的特徴ｇθ（指向的特徴ｇ0°、指向的特徴ｇ2 2.5°、指向的特徴ｇ45°、指向的特徴ｇ67.5°、指向的特徴ｇ90°、指向的特徴ｇ112.5 °、指向的特徴ｇ135°、指向的特徴ｇ157.5°）として得る。なお、フィルタリング処理時に設定される方向θの数は２つ以上であれば、８つに限定されない。また、フィルタリング処理は、画像ｆ内の各方向θの線状の暗部に対して高いフィルタ応答をもつものであれば、Gaborフィルタ処理に限定されない。The filter 41 performs Gabor filter processing for each of the eight directions θ (0 °, 22.5 °, 45 °, 67.5 °, 90 °, 112.5 °, 135 °, 157.5 °) with respect to the input image f of the palm region. Each filter response (luminance value) is assigned to a directional feature g θ (directional feature g 0 ° , directional feature g 2 2.5 ° , directional feature g 45 ° , directional feature g 67.5 ° , directional feature g 90 ° , directional feature g 112.5 ° , directional feature g 135 ° , directional feature g 157.5 ° ). Note that the number of directions θ set during the filtering process is not limited to eight as long as it is two or more. The filtering process is not limited to the Gabor filter process as long as it has a high filter response with respect to a linear dark part in each direction θ in the image f.

方向毎指向的特徴正規化処理部４２は、フィルタ４１から抽出される各指向的特徴ｇθに対してそれぞれ正規化を行う。Direction each directional feature normalization processing unit 42 performs respective normalized for each directional characteristic g theta extracted from the filter 41.

各点最大値選択部４３は、方向毎指向的特徴正規化処理部４２から出力される各指向的特徴ｇθにより無指向的特徴gを生成する。例えば、各点最大値選択部４３は、式１に示すように、方向毎指向的特徴正規化処理部４２から出力される各指向的特徴ｇθ（i,j）のうち、最大の指向的特徴ｍａｘθ｛ｇθ（i,j）｝を無指向的特徴g(i,j)として出力する。なお、iは、手のひら領域内の各画素の位置を２次元座標上の位置に対応させたときのその２次元座標の横軸方向の位置を示し、jは、その２次元座標の縦軸方向の位置を示す。Maximum value selector 43 points produces a non-directional characteristics g of each directional characteristic g theta outputted from the direction each directional feature normalization processing unit 42. For example, each point maximum value selection unit 43, as shown in Equation 1, among the directional features g θ (i, j) output from the directional feature normalization processing unit 42 for each direction, The feature max θ {g θ (i, j)} is output as an omnidirectional feature g (i, j). Note that i indicates the position in the horizontal axis direction of the two-dimensional coordinate when the position of each pixel in the palm region corresponds to the position on the two-dimensional coordinate, and j indicates the vertical axis direction of the two-dimensional coordinate. Indicates the position.

Figure 0006431044
Figure 0006431044

２値化部４４は、式２に示すように、各点最大値選択部４３から出力される無指向的特徴g(i,j)が正の値であるとき、１を無指向的面特徴b(i,j)として出力し、無指向的特徴g(i,j)が正の値以外の値であるとき、０を無指向的面特徴b(i,j)として出力する。このとき得られた無指向的面特徴ｂは、記憶部７に記憶される。  As shown in Equation 2, when the omnidirectional feature g (i, j) output from each point maximum value selection unit 43 is a positive value, the binarizing unit 44 sets 1 to an omnidirectional surface feature. b (i, j) is output, and when the omnidirectional feature g (i, j) is a value other than a positive value, 0 is output as the omnidirectional surface feature b (i, j). The omnidirectional surface feature b obtained at this time is stored in the storage unit 7.

Figure 0006431044
Figure 0006431044

なお、上記２値化部４４では、単純な定数０による閾値処理によって２値化処理を行っているが、より高度なAdaptive-thresholding法を用いて２値化処理を行ってもよい。  In the binarization unit 44, the binarization process is performed by a threshold process using a simple constant 0. However, the binarization process may be performed using a more advanced adaptive-thresholding method.

細線化部４５は、式３に示すように、無指向的面特徴bに対して、細線化処理（skeltonizing）を行うことにより、無指向的線特徴ＬＦを得る。なお、skelは、細線化処理を表す。また、このとき得られた無指向的線特徴ＬＦは、記憶部７に記憶される。また、線特徴とは、線状からなる画像である。  The thinning unit 45 obtains an omnidirectional line feature LF by performing thinning processing (skeltonizing) on the omnidirectional surface feature b as shown in Expression 3. Note that skel represents a thinning process. Further, the omnidirectional line feature LF obtained at this time is stored in the storage unit 7. A line feature is an image made up of lines.

Figure 0006431044
Figure 0006431044

図１に示す照合処理部５は、図４に示すように、細線化部４５から出力され記憶部７に記憶されている無指向的線特徴ＬＦと、予め登録され記憶部７に記憶されている登録無指向的線特徴ＴＬＦとの類似度scoreを求める。  As shown in FIG. 4, the collation processing unit 5 shown in FIG. 1 includes an omnidirectional line feature LF output from the thinning unit 45 and stored in the storage unit 7 and pre-registered and stored in the storage unit 7. A similarity score with a registered non-directional line feature TLF is obtained.

図１に示すスコア判定部６は、類似度scoreが閾値以上であるとき、本人であると判定する。  The score determination unit 6 illustrated in FIG. 1 determines that the person is the person when the similarity score is equal to or greater than a threshold value.

掌紋に相当する輝度値は静脈に相当する輝度値よりも大きくなる傾向にある。そのため、フィルタ４１から抽出される各指向的特徴ｇθに対して正規化が行われると、各指向的特徴ｇθにおいて、静脈に相当する輝度値に比べて掌紋に相当する輝度値が小さくなる。そして、正規化後の各指向的特徴ｇθが用いられて生成される無指向的線特徴ＬＦは、掌紋の影響が抑えられたものになる。そのため、掌紋の影響を抑えて本人であるか否かの判定を行うことができるため、認証精度を向上させることができる。すなわち、本開示の実施形態の生体認証装置１によれば、画像から掌紋を示す特徴を物理的に分離する方法を適用できない場合であっても、他人受入率が高くなることを抑えることができる。また、特に被験者の手のひらにおいてメラニンが強く沈着し、指向的特徴ｇθに掌紋が多く含まれる場合、特に、無指向的線特徴ＬＦにおける掌紋の影響を抑えて本人であるか否かの判定を行うことができるため、他人受入率を低減することができる。The luminance value corresponding to the palm print tends to be larger than the luminance value corresponding to the vein. Therefore, when normalization is performed for each directional characteristic g theta extracted from the filter 41, in each directional characteristic g theta, intensity values corresponding to the palm print is smaller than the luminance value corresponding to the vein . Then, omnidirectionally line feature LF each directional characteristic g theta after the normalization is generated used will what influence the palm print is suppressed. Therefore, since it is possible to determine whether or not the person is the person while suppressing the influence of the palm print, the authentication accuracy can be improved. That is, according to the biometric authentication device 1 of the embodiment of the present disclosure, it is possible to suppress an increase in the acceptance rate of others even when a method for physically separating a feature indicating a palmprint from an image cannot be applied. . In particular deposited strong melanin in the palm of the subject, if the palm print is abundant in directional characteristics g theta, in particular, whether a person with reduced influence of a palm print in the non-oriented line feature LF determination Since it can be performed, the acceptance rate of others can be reduced.

また、フィルタ４１から抽出される各指向的特徴ｇθに対して正規化が行われると、各指向的特徴ｇθにおいて、掌紋に相当する輝度値に比べて静脈に相当する輝度値が大きくなる。そして、正規化後の各指向的特徴ｇθが用いられて生成される無指向的線特徴ＬＦは、静脈が強調されたものになる。そのため、掌紋に比べて多様性が高い静脈を強調させて本人であるか否かの判定を行うことができるので、本人拒否率を低減することができる。Further, when normalization is performed for each directional characteristic g theta extracted from the filter 41, in each directional characteristic g theta, intensity values corresponding to the vein is larger than the luminance value corresponding to a palm print . Then, omnidirectionally line feature LF each directional characteristic g theta after the normalization is generated used will what vein is emphasized. Therefore, it is possible to determine whether or not the person is the person by emphasizing veins that are more diverse than the palm print, so that the person rejection rate can be reduced.

図５は、本開示の実施形態の生体認証装置１を構成するハードウェアの一例を示す図である。  FIG. 5 is a diagram illustrating an example of hardware configuring the biometric authentication device 1 according to the embodiment of the present disclosure.

図５に示すように、生体認証装置１を構成するハードウェアは、制御部１２０１と、記憶部１２０２と、記録媒体読取装置１２０３と、入出力インタフェース１２０４と、通信インタフェース１２０５とを備え、それらがバス１２０６によってそれぞれ接続されている。なお、画像処理装置１を構成するハードウェアは、クラウドなどを用いて実現してもよい。  As shown in FIG. 5, the hardware constituting the biometric authentication device 1 includes a control unit 1201, a storage unit 1202, a recording medium reading device 1203, an input / output interface 1204, and a communication interface 1205. Each is connected by a bus 1206. Note that the hardware configuring the image processing apparatus 1 may be realized using a cloud or the like.

制御部１２０１は、例えば、Central Processing Unit（ＣＰＵ）、マルチコアＣＰＵ、プログラマブルなデバイス（Field Programmable Gate Array（ＦＰＧＡ）、Programmable Logic Device（ＰＬＤ）など）を用いることが考えられ、図１に示す領域特定部３、特徴抽出部４、照合処理部５、及びスコア判定部６に相当する。  For example, the control unit 1201 may use a Central Processing Unit (CPU), a multi-core CPU, and a programmable device (Field Programmable Gate Array (FPGA), Programmable Logic Device (PLD), etc.). This corresponds to the unit 3, the feature extraction unit 4, the matching processing unit 5, and the score determination unit 6.

記憶部１２０２は、図１に示す記憶部７に相当し、例えばRead Only Memory（ＲＯＭ）、Random Access Memory（ＲＡＭ）などのメモリやハードディスクなどが考えられる。なお、記憶部１２０２は、実行時のワークエリアとして用いてもよい。また、生体認証装置１の外部に他の記憶部を設けてもよい。  The storage unit 1202 corresponds to the storage unit 7 illustrated in FIG. 1 and may be a memory such as a read only memory (ROM) or a random access memory (RAM), a hard disk, or the like. Note that the storage unit 1202 may be used as a work area at the time of execution. Further, another storage unit may be provided outside the biometric authentication device 1.

記録媒体読取装置１２０３は、制御部１２０１の制御により、記録媒体１２０７に記録されるデータを読み出したり、記録媒体１２０７にデータを書き込んだりする。また、着脱可能な記録媒体１２０７は、コンピュータで読み取り可能なnon-transitory（非一時的）な記録媒体であって、例えば、磁気記録装置、光ディスク、光磁気記録媒体、半導体メモリなどが考えられる。磁気記録装置は、例えば、ハードディスク装置（ＨＤＤ）などが考えられる。光ディスクは、例えば、Digital Versatile Disc（ＤＶＤ）、ＤＶＤ−ＲＡＭ、Compact Disc Read Only Memory（ＣＤ−ＲＯＭ）、ＣＤ−Ｒ(Recordable)／ＲＷ（ReWritable）などが考えられる。光磁気記録媒体は、例えば、Magneto-Optical disk（ＭＯ）などが考えられる。なお、記憶部１２０２もnon-transitory（非一時的）な記録媒体に含まれる。  The recording medium reading device 1203 reads data recorded on the recording medium 1207 and writes data to the recording medium 1207 under the control of the control unit 1201. The removable recording medium 1207 is a non-transitory recording medium that can be read by a computer. For example, a magnetic recording device, an optical disk, a magneto-optical recording medium, a semiconductor memory, and the like are conceivable. As the magnetic recording device, for example, a hard disk device (HDD) can be considered. As the optical disk, for example, Digital Versatile Disc (DVD), DVD-RAM, Compact Disc Read Only Memory (CD-ROM), CD-R (Recordable) / RW (ReWritable), and the like can be considered. As the magneto-optical recording medium, for example, a Magneto-Optical disk (MO) can be considered. Note that the storage unit 1202 is also included in a non-transitory recording medium.

入出力インタフェース１２０４は、入出力部１２０８が接続され、ユーザにより入出力部１２０８から入力された情報をバス１２０６を介して制御部１２０１に送る。また、入出力インタフェース１２０４は、制御部１２０１から送られてくる情報をバス１２０６を介して入出力部１２０８に送る。  The input / output interface 1204 is connected to the input / output unit 1208, and sends information input from the input / output unit 1208 by the user to the control unit 1201 via the bus 1206. The input / output interface 1204 sends information sent from the control unit 1201 to the input / output unit 1208 via the bus 1206.

入出力部１２０８は、図１に示す画像取得部２に相当し、例えば、撮像装置などが考えられる。また、入出力部１２０８は、例えば、キーボード、ポインティングデバイス（マウスなど）、タッチパネル、Cathode Ray Tube（ＣＲＴ）ディスプレイ、プリンタなどが考えられる。  The input / output unit 1208 corresponds to the image acquisition unit 2 illustrated in FIG. 1 and may be an imaging device, for example. The input / output unit 1208 may be, for example, a keyboard, a pointing device (such as a mouse), a touch panel, a Cathode Ray Tube (CRT) display, or a printer.

通信インタフェース１２０５は、Local Area Network（ＬＡＮ）接続やインターネット接続を行うためのインタフェースである。また、通信インタフェース１２０５は必要に応じ、他のコンピュータとの間のＬＡＮ接続やインターネット接続や無線接続を行うためのインタフェースとして用いてもよい。  The communication interface 1205 is an interface for performing Local Area Network (LAN) connection or Internet connection. Further, the communication interface 1205 may be used as an interface for performing a LAN connection, Internet connection, or wireless connection with another computer as necessary.

このようなハードウェアを有するコンピュータを用いることによって、生体認証装置１が行う各種処理機能が実現される。この場合、生体認証装置１が行う各種処理機能の内容を記述したプログラムをコンピュータで実行することにより、上記各処理機能（例えば、領域特定部３、特徴抽出部４、照合処理部５、スコア判定部６）がコンピュータ上で実現される。各種処理機能の内容を記述したプログラムは、記憶部１２０２や記録媒体１２０７に格納しておくことができる。  By using a computer having such hardware, various processing functions performed by the biometric authentication device 1 are realized. In this case, each processing function (for example, the area specifying unit 3, the feature extracting unit 4, the matching processing unit 5, the score determination) is executed by executing a program describing the contents of various processing functions performed by the biometric authentication device 1 on a computer. Part 6) is realized on a computer. A program describing the contents of various processing functions can be stored in the storage unit 1202 or the recording medium 1207.

プログラムを流通させる場合には、例えば、そのプログラムが記録されたＤＶＤ、ＣＤ−ＲＯＭなどの記録媒体１２０７が販売される。また、プログラムをサーバコンピュータの記憶装置に記録しておき、ネットワークを介して、サーバコンピュータから他のコンピュータにそのプログラムを転送することもできる。  When distributing the program, for example, a recording medium 1207 such as a DVD or a CD-ROM in which the program is recorded is sold. It is also possible to record the program in a storage device of the server computer and transfer the program from the server computer to another computer via a network.

プログラムを実行するコンピュータは、例えば、記録媒体１２０７に記録されたプログラム、又は、サーバコンピュータから転送されたプログラムを、記憶部１２０２に記憶する。そして、コンピュータは、記憶部１２０２からプログラムを読み取り、プログラムに従った処理を実行する。なお、コンピュータは、記録媒体１２０７から直接プログラムを読み取り、そのプログラムに従った処理を実行することもできる。また、コンピュータは、サーバコンピュータからプログラムが転送されるごとに、逐次、受け取ったプログラムに従った処理を実行することもできる。  The computer that executes the program stores, for example, the program recorded in the recording medium 1207 or the program transferred from the server computer in the storage unit 1202. The computer reads the program from the storage unit 1202 and executes processing according to the program. The computer can also read the program directly from the recording medium 1207 and execute processing according to the program. Further, each time the program is transferred from the server computer, the computer can sequentially execute processing according to the received program.

なお、本開示の実施形態では、手のひらの静脈を用いて認証を行う画像処理装置を例示して説明したが、これに限らず、生体のその他の特徴検出部位であればどこでもよい。  In the embodiment of the present disclosure, the image processing apparatus that performs authentication using the palm vein has been described as an example. However, the present invention is not limited thereto, and any other feature detection site of a living body may be used.

たとえば、生体のその他の特徴検出部位は、静脈に限らず、生体の血管像や、生体の紋様、生体の指紋や掌紋、足の裏、手足の指、手足の甲、手首、腕などであってもよい。  For example, other feature detection parts of a living body are not limited to veins, but include a blood vessel image of a living body, a pattern of a living body, a fingerprint or palm print of a living body, a sole, a toe, a back of a limb, a wrist, an arm, etc. May be.

なお、認証に静脈を用いる場合、生体のその他の特徴検出部位は、静脈を観察可能な部位であればよい。  In addition, when using a vein for authentication, the other characteristic detection site | part of a biological body should just be a site | part which can observe a vein.

なお、生体情報を特定可能な生体のその他の特徴検出部位であれば認証に有利である。たとえば、手のひらや顔などであれば、取得した画像から部位を特定可能である。また、上述の実施の形態は、実施の形態の要旨を逸脱しない範囲内において種々の変更を加えることができる。さらに、上述の実施の形態は、多数の変形、変更が当業者にとって可能であり、説明した正確な構成および応用例に限定されるものではない。  Any other feature detection part of a living body that can specify biological information is advantageous for authentication. For example, in the case of a palm or face, a part can be specified from the acquired image. Further, the above-described embodiment can be variously modified within a range not departing from the gist of the embodiment. Further, the above-described embodiments can be modified and changed by those skilled in the art, and are not limited to the exact configurations and application examples described.

１ 生体認証装置
２ 画像取得部
３ 領域特定部
４ 特徴抽出部
５ 照合処理部
６ スコア判定部
７ 記憶部
４１ フィルタ
４２ 方向毎指向的特徴正規化処理部
４３ 各点最大値選択部
４４ ２値化部
４５ 細線化部DESCRIPTION OF SYMBOLS 1 Biometric authentication apparatus 2 Image acquisition part 3 Area | region specific part 4 Feature extraction part 5 Collation processing part 6 Score determination part 7 Storage part 41 Filter 42 Directional characteristic normalization processing part 43 Each point maximum value selection part 44 Binarization Part 45 Thinning part

Claims (3)

入力される画像から複数の指向的特徴を抽出するフィルタと、
前記フィルタにより抽出される複数の指向的特徴に対して静脈に相当する輝度値よりも大きくなる傾向にある掌紋に相当する輝度値が静脈に相当する輝度値に比べて小さくなるように正規化を行う方向毎指向的特徴正規化処理部と、
前記方向毎指向的特徴正規化処理部から出力される複数の指向的特徴により無指向的特徴を生成する無指向的特徴生成部と、
前記無指向的特徴と記憶部に記憶されている登録無指向的特徴との類似度を求める照合処理部と、
前記類似度を用いて本人であるか否かの判定を行う判定部と、
を備える生体認証装置。 A filter that extracts multiple directional features from the input image;
For a plurality of directional features extracted by the filter, normalization is performed so that a luminance value corresponding to a palm print that tends to be larger than a luminance value corresponding to a vein is smaller than a luminance value corresponding to a vein. A direction-specific feature normalization processing unit to perform,
An omnidirectional feature generation unit that generates an omnidirectional feature by a plurality of directional features output from the directional feature normalization processing unit for each direction;
A collation processing unit for obtaining a similarity between the omnidirectional feature and a registered omnidirectional feature stored in the storage unit;
A determination unit that determines whether or not the person is the person using the similarity,
A biometric authentication device. コンピュータが、
入力される画像から複数の指向的特徴を抽出し、
前記抽出した複数の指向的特徴に対して静脈に相当する輝度値よりも大きくなる傾向にある掌紋に相当する輝度値が静脈に相当する輝度値に比べて小さくなるように正規化を行い、
前記正規化した複数の指向的特徴により無指向的特徴を生成し、
前記無指向的特徴と記憶部に記憶されている登録無指向的特徴との類似度を求め、
前記類似度を用いて本人であるか否かの判定を行う、
ことを特徴とする生体認証方法。 Computer
Extract multiple directional features from the input image,
Normalizing the extracted plurality of directional features so that the luminance value corresponding to the palmprint that tends to be larger than the luminance value corresponding to the vein is smaller than the luminance value corresponding to the vein ,
Generating omnidirectional features by the plurality of normalized directional features;
Obtaining a similarity between the omnidirectional feature and the registered omnidirectional feature stored in the storage unit;
It is determined whether or not the person is the person using the similarity.
A biometric authentication method. コンピュータに、
入力される画像から複数の指向的特徴を抽出し、
前記抽出した複数の指向的特徴に対して静脈に相当する輝度値よりも大きくなる傾向にある掌紋に相当する輝度値が静脈に相当する輝度値に比べて小さくなるように正規化を行い、
前記正規化した複数の指向的特徴により無指向的特徴を生成し、
前記無指向的特徴と記憶部に記憶されている登録無指向的特徴との類似度を求め、
前記類似度を用いて本人であるか否かの判定を行う、
ことを実行させるためのプログラム。 On the computer,
Extract multiple directional features from the input image,
Normalizing the extracted plurality of directional features so that the luminance value corresponding to the palmprint that tends to be larger than the luminance value corresponding to the vein is smaller than the luminance value corresponding to the vein ,
Generating omnidirectional features by the plurality of normalized directional features;
Obtaining a similarity between the omnidirectional feature and the registered omnidirectional feature stored in the storage unit;
It is determined whether or not the person is the person using the similarity.
A program to make things happen.
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